code: def __init__(self, env, vector_index, framestack):
      super().__init__(env)
      self.framestack = framestack
      # 49=7x7 field of vision; 11=object types; 6=colors; 3=state types.
      # +4: Direction.
      self.single_frame_dim = 49 * (11 + 6 + 3) + 4
      self.init_x = None
      self.init_y = None
      self.x_positions = []
      self.y_positions = []
      self.x_y_delta_buffer = deque(maxlen=100)
      self.vector_index = vector_index
      self.frame_buffer = deque(maxlen=self.framestack)
      for _ in range(self.framestack):
          self.frame_buffer.append(np.zeros((self.single_frame_dim,)))
  
      self.observation_space = gym.spaces.Box(
          0.0,
          1.0,
          shape=(self.single_frame_dim * self.framestack,),
          dtype=np.float32,
      )
  
imports: from collections import deque
  import gymnasium
  import minigrid
  import numpy
  import sys
  import unittest
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  import ray.rllib.algorithms.ppo
  from ray.rllib.utils.test_utils import check_learning_achieved, framework_iterator
  from ray.rllib.utils.numpy import one_hot
  from ray.tune import register_env
label_desc: Use image node_id 1 to create a new OneHotWrapper object from inherited base classes: gym.core with example: obj = OneHotWrapper(env, vector_index, framestack)